Abstract-Ridesharing offers the opportunity to make more efficient use of vehicles while preserving the benefits of individual mobility. Presenting ridesharing as a viable option for commuters, however, requires minimizing certain inconvenience factors. One of these factors includes detours which result from picking up and dropping off additional passengers. This paper proposes a method which aims to best utilize ridesharing potential while keeping detours below a specific limit. The method specifically targets ridesharing systems on a very large scale and with a high degree of dynamics which are difficult to address using classical approaches known from operations research. For this purpose, the road network is divided into distinct partitions which define the search space for ride matches. The size and shape of the partitions depend on the topology of the road network as well as on two free parameters. This allows optimizing the partitioning with regard to sharing potential utilization and inconvenience minimization. Match making is ultimately performed using an agent-based approach. As a case study, the algorithm is applied to investigate the potential for taxi sharing in Singapore. This is done by considering about 110 000 daily trips and allowing up to two sharing partners. The outcome shows that the number of trips could be reduced by 42% resulting in a daily mileage savings of 230 000 km. It is further shown that the presented approach exceeds the mileage savings achieved by a greedy heuristic by 6% while requiring 30% lower computational efforts.

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A charging and dispatching strategy for optimizing profits from V2G is presented. This optimization strategy builds on temporally resolved electricity market data. A case study shows that this method turns a S$ 1000 annual loss into a S$ 130 profit. Sensitivity analyses indicate potential for further increase of profitability. Employing this strategy in other countries is assumed to yield much greater profits. a r t i c l e i n f o b s t r a c tEmploying electric vehicles as short-term energy storage could improve power system stability and at the same time create a new income source for vehicle owners. In this paper, the economic viability of this concept referred to as Vehicle-to-Grid is investigated. For this purpose, a price-responsive charging and dispatching strategy built upon temporally resolved electricity market data is presented. This concept allows vehicle owners to maximize returns by restricting market participation to profitable time periods. As a case study, this strategy is then applied using the example of Singapore. It is shown that an annual loss of S$ 1000 resulting from a non-price-responsive strategy as employed in previous works can be turned into a S$ 130 profit by applying the price-responsive approach. In addition to this scenario, realistic mobility patterns which restrict the temporal availability of vehicles are considered. In this case, profits in the range of S$ 21eS$ 121 are achievable. Returns in this order of magnitude are not expected to make Vehicle-to-Grid a viable business case, sensitivity analyses, however, show that improved technical parameters could increase profitability. It is further assumed that employing the priceresponsive strategy to other national markets may yield significantly greater returns.

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As a result of growing markets for electric vehicles and residential batteries for buffering energy from photovoltaics, the number of grid-integrated lithium-ion batteries has been continuously increasing in the past years. Apart from their primary purpose, these batteries may also be employed to provide services to the power grid in terms of peak shaving or frequency regulation. The profitability of such services for the battery owner, however, remains a controversial issue. Particularly battery degradation resulting from increased energy throughput is discussed as a major impediment for profitable operation. This paper presents a scheduling approach which considers the non-linear dependencies of battery aging from various operation parameters along with real-time prices and price forecasts for computing optimal charging/dispatching schedules. The methodology is applied to price-data obtained from four different electricity markets. The investigation partly confirms existing profitability concerns but further shows that explicit consideration of battery degradation can yield profitable outcomes. Various scenarios using aggregated and locational marginal prices as well as different forecasting horizons and time resolutions are explored to identify favorable operating conditions.

The large-scale introduction of plug-in electric vehicles (PEV) may pose challenges to power system operators by causing grid congestion or voltage fluctuations. This work presents a simulation-based approach for investigating the impact of transport electrification on power grids. The framework consists of an agent-based traffic simulation which is coupled with a power system simulation through the IEEE Standard High Level Architecture. As detailed power grid information is often unavailable, the framework further contains a method for synthesizing power networks from tempospatially resolved demand data. Using a high-performance computing infrastructure, the approach allows simulating the traffic and power system on the scale of a megacity faster than real-time. An application to the example of Singapore shows that grid congestion and voltage drops are observed on the low voltage level while the high and medium voltage grid remain unaffected. The presented framework may facilitate infrastructure decisions and support the development of smart charging strategies minimizing power grid impact.

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This work presents a modular power system planning and power flow simulation framework for the generation and evaluation of power network models (PNM) using spatially resolved demand data. It targets users who want to study large-scale power grids having only limited information on the actual power system. Besides creating cost minimal PNMs, users are able to flexibly configure the framework to produce PNMs individually tailored to their specific use cases. Both greenfield and expansion planning are possible. The framework further comprises a built-in ac power flow simulation designed to simulate power flows in large-scale networks. This allows users to conduct a great variety of simulation studies on entire power systems, which would otherwise not be possible without access to comprehensive power grid data. Apart from the presentation of the methodology, this work comprises a demonstration of the power system planning process at the example of Singapore. The investigation shows that the framework is capable of generating a network that matches the topological and electrical metrics of the Singapore power grid.